T-regulatory cells predict clinical outcome in soft tissue sarcoma patients: a clinico-pathological study.

BACKGROUND: Soft tissue sarcomas (STS) are generally considered non-immunogenic, although specific subtypes respond to immunotherapy. Antitumour response within the tumour microenvironment relies on a balance between inhibitory and activating signals for tumour-infiltrating lymphocytes (TILs). This study analysed TILs and immune checkpoint molecules in STS, and assessed their prognostic impact regarding local recurrence (LR), distant metastasis (DM), and overall survival (OS). METHODS: One-hundred and ninety-two surgically treated STS patients (median age: 63.5 years; 103 males [53.6%]) were retrospectively included. Tissue microarrays were constructed, immunohistochemistry for PD-1, PD-L1, FOXP3, CD3, CD4, and CD8 performed, and staining assessed with multispectral imaging. TIL phenotype abundance and immune checkpoint markers were correlated with clinical and outcome parameters (LR, DM, and OS). RESULTS: Significant differences between histology and all immune checkpoint markers except for FOXP3+ and CD3-PD-L1+ cell subpopulations were found. Higher levels of PD-L1, PD-1, and any TIL phenotype were found in myxofibrosarcoma as compared to leiomyosarcoma (all p < 0.05). The presence of regulatory T cells (Tregs) was associated with increased LR risk (p = 0.006), irrespective of margins. Other TILs or immune checkpoint markers had no significant impact on outcome parameters. CONCLUSIONS: TIL and immune checkpoint marker levels are most abundant in myxofibrosarcoma. High Treg levels are independently associated with increased LR risk, irrespective of margins.

Cell2Grid: an efficient, spatial, and convolutional neural network-ready representation of cell segmentation data.

Purpose: Cell segmentation algorithms are commonly used to analyze large histologic images as they facilitate interpretation, but on the other hand they complicate hypothesis-free spatial analysis. Therefore, many applications train convolutional neural networks (CNNs) on high-resolution images that resolve individual cells instead, but their practical application is severely limited by computational resources. In this work, we propose and investigate an alternative spatial data representation based on cell segmentation data for direct training of CNNs. Approach: We introduce and analyze the properties of Cell2Grid, an algorithm that generates compact images from cell segmentation data by placing individual cells into a low-resolution grid and resolves possible cell conflicts. For evaluation, we present a case study on colorectal cancer relapse prediction using fluorescent multiplex immunohistochemistry images. Results: We could generate Cell2Grid images at 5 - µ m resolution that were 100 times smaller than the original ones. Cell features, such as phenotype counts and nearest-neighbor cell distances, remain similar to those of original cell segmentation tables ( p < 0.0001 ). These images could be directly fed to a CNN for predicting colon cancer relapse. Our experiments showed that test set error rate was reduced by 25% compared with CNNs trained on images rescaled to 5 µ m with bilinear interpolation. Compared with images at 1 - µ m resolution (bilinear rescaling), our method reduced CNN training time by 85%. Conclusions: Cell2Grid is an efficient spatial data representation algorithm that enables the use of conventional CNNs on cell segmentation data. Its cell-based representation additionally opens a door for simplified model interpretation and synthetic image generation.

Influence of tumor-infiltrating immune cells on local control rate, distant metastasis, and survival in patients with soft tissue sarcoma.

Soft tissue sarcomas (STS) are considered non-immunogenic, although distinct entities respond to anti-tumor agents targeting the tumor microenvironment. This study's aims were to investigate relationships between tumor-infiltrating immune cells and patient/tumor-related factors, and assess their prognostic value for local recurrence (LR), distant metastasis (DM), and overall survival (OS). One-hundred-eighty-eight STS-patients (87 females [46.3%]; median age: 62.5 years) were retrospectively analyzed. Tissue microarrays (in total 1266 cores) were stained with multiplex immunohistochemistry and analyzed with multispectral imaging. Seven cell types were differentiated depending on marker profiles (CD3+, CD3+ CD4+ helper, CD3+ CD8+ cytotoxic, CD3+ CD4+ CD45RO+ helper memory, CD3+ CD8+ CD45RO+ cytotoxic memory T-cells; CD20 + B-cells; CD68+ macrophages). Correlations between phenotype abundance and variables were analyzed. Uni- and multivariate Fine&Gray and Cox-regression models were constructed to investigate prognostic variables. Model calibration was assessed with C-index. IHC-findings were validated with TCGA-SARC gene expression data of genes specific for macrophages, T- and B-cells. B-cell percentage was lower in patients older than 62.5 years (p = .013), whilst macrophage percentage was higher (p = .002). High B-cell (p = .035) and macrophage levels (p = .003) were associated with increased LR-risk in the univariate analysis. In the multivariate setting, high macrophage levels (p = .014) were associated with increased LR-risk, irrespective of margins, age, gender or B-cells. Other immune cells were not associated with outcome events. High macrophage levels were a poor prognostic factor for LR, irrespective of margins, B-cells, gender and age. Thus, anti-tumor, macrophage-targeting agents may be applied more frequently in tumors with enhanced macrophage infiltration.